{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 一 项目简介\n",
    "- 1 该项目使用Labelimg进行数据标注，自定义数据集；\n",
    "- 2 使用Paddlex将数据集划分为训练集、测试集；\n",
    "- 3 使用PaddleDetection目标检测套件训练模型；\n",
    "- 4 最后导出模型，通过PaddleLite生成.nb文件，部署到手机上；\n",
    "- 5 安卓部署详细操作；\n",
    "- （1）配置系统环境\n",
    "- （2）下载PaddleLite-dome\n",
    "- （3）下载Android Studio开发环境并配置软件环境\n",
    "- （4）dome调试\n",
    "- （5）根据dome，加入自己的模型，修改配置，实现自己的dome，并调试\n",
    "- （6）将APP打包成可安装程序.apk文件\n",
    "- 实现飞桨框架深度学习模型从0到1的全流程。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 二 数据标注\n",
    "- 个人建议安装Anaconda便于对包环境的管理，在Anaconda环境中安装Labelimg（b站有这类教学视频，若还有不明白的地方可评论区解答）\n",
    "- 下面讲解安装成功后操作流程\n",
    "- 1 新建数据集文件夹：JPEGImages文件存放事先准备好的图片，Annotations文件存放xml标注文件（未标注时此文件为空）\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/2a4d694177f34f9db0803fc20b1706f68936d2f4282a43fa84e60f9f3e5baef3)\n",
    "\n",
    "- 2 打开Labelimg：点击Change Save Dir找到刚刚创建的Annotations文件；点击Open Dir找到JPEGImages文件；快捷键按D，拖拽选中区域，并在弹框内打标签；点击Next Image对下一张图片进行标注（此时会弹出是否保存的提示框，可勾选View->Auto Save mode，默认将每张图片标注完后自动保存）\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/4df4a9df88e1461b8a7310cdd9fc457174ed4e96c0f84108babe41c89a34cb31)\n",
    "\n",
    "- 3 上述步骤完成后，Annotations文件中会产生一堆xml文件，格式如下，\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/b5a62383c4c84248ad7b541afd8fb7fd1294ca7ca46b4691bce937476a31110b)\n",
    "\n",
    "- 4 最后将文件压缩上传到aistudio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 三 paddlex划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#将数据集进行解压（此处可换成你自己标注的数据集）\r\n",
    "!unzip data/data88819/VOC_MASK.zip -d masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 准备一些可能用到的工具库\r\n",
    "import xml.etree.cElementTree as ET\r\n",
    "import os\r\n",
    "import cv2\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import matplotlib.patches as patches\r\n",
    "import math\r\n",
    "import paddle.fluid as fluid\r\n",
    "import time\r\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 导入paddlex\r\n",
    "!pip install paddlex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Split Done.\u001b[0m\r\n",
      "\u001b[0mTrain samples: 5567\u001b[0m\r\n",
      "\u001b[0mEval samples: 1590\u001b[0m\r\n",
      "\u001b[0mTest samples: 795\u001b[0m\r\n",
      "\u001b[0mSplit files saved in masks/VOC_MASK\u001b[0m\r\n",
      "\u001b[0m\u001b[0m"
     ]
    }
   ],
   "source": [
    "# 使用paddlex进行数据集的划分训练集、验证集、测试集\r\n",
    "!paddlex --split_dataset --format VOC --dataset_dir masks/VOC_MASK --val_value 0.2 --test_value 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "执行完上述代码可以看到，masks/VOC_MASK目录下会自动生成以下文件：\n",
    "\n",
    "- val_list.txt 验证集列表\n",
    "- test_list.txt 测试集列表\n",
    "- labels.txt 标签列表\n",
    "- train_list.txt 训练集列表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 四 PaddleDetection目标检测套件使用，进行模型的创建、训练、导出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1 PaddleDetection下载、配置、数据集文件的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 下载PaddleDetection到本地\r\n",
    "# 为避免因PaddleDetection更新导致代码报错，我将我的PaddleDetection版本挂载到数据集里了data/data88824/model_PaddleDetection.zip，可解压使用\r\n",
    "!git clone https://gitee.com/paddlepaddle/PaddleDetection.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 把PaddleDetection转移到work目录下，可以持久保存\r\n",
    "!mv PaddleDetection work/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#配置PaddleDetection环境\r\n",
    "!pip install -r work/PaddleDetection/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#将数据集转到PaddleDetection/dataset，专门存放和处理数据集的文件\r\n",
    "!cp -r masks/VOC_MASK work/PaddleDetection/dataset/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/work/PaddleDetection\n"
     ]
    }
   ],
   "source": [
    "%cd work/PaddleDetection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 调整下标注文件命名，与PaddleDetection默认的一致（处理成相同文件名）\r\n",
    "!mv dataset/VOC_MASK/labels.txt dataset/VOC_MASK/label_list.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 2 修改模型文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 修改模型主文件ssd_mobilenet_v1_300_120e_voc.yml\n",
    "此处用的SSD模型，主干网络是Mobilnet（比VGG更轻量级的网络，适合于移动端部署），图片输入尺寸300x300，数据集格式voc\n",
    "\n",
    "- ssd_mobilenet_v1_300_120e_voc.yml 文件入口\n",
    "\n",
    "文件地址：work/PaddleDetection/configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml\n",
    "```\n",
    "_BASE_: [\n",
    "  '../datasets/voc.yml',\n",
    "  '../runtime.yml',\n",
    "  '_base_/optimizer_120e.yml',\n",
    "  '_base_/ssd_mobilenet_v1_300.yml',\n",
    "  '_base_/ssd_mobilenet_reader.yml',\n",
    "]\n",
    "weights: output/ssd_mobilenet_v1_300_120e_voc/model_final # 模型权重文件保存路径\n",
    "```\n",
    "\n",
    "- voc.yml 主要说明了训练数据和验证数据的路径\n",
    "文件地址：work/PaddleDetection/configs/datasets/voc.yml\n",
    "```\n",
    "metric: VOC\n",
    "map_type: 11point\n",
    "num_classes: 3  # 类别数+1（背景类）\n",
    "\n",
    "TrainDataset:\n",
    "  !VOCDataSet\n",
    "    dataset_dir: dataset/VOC_MASK  # 数据集存放位置\n",
    "    anno_path: train_list.txt    # 训练集\n",
    "    label_list: label_list.txt   # 标签\n",
    "    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']\n",
    "\n",
    "EvalDataset:\n",
    "  !VOCDataSet\n",
    "    dataset_dir: dataset/VOC_MASK   # 数据集存放位置\n",
    "    anno_path: val_list.txt    # 验证集\n",
    "    label_list: label_list.txt  # 标签\n",
    "    data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']\n",
    "\n",
    "TestDataset:\n",
    "  !ImageFolder\n",
    "    anno_path: dataset/VOC_MASK/label_list.txt  # 标签文件所在地址\n",
    "```\n",
    "\n",
    "- runtime.yml 主要说明了公共的运行参数，比如说是否使用GPU、每多少个epoch存储checkpoint等。\n",
    "文件地址：work/PaddleDetection/configs/runtime.yml\n",
    "```\n",
    "use_gpu: true  # 使用gpu\n",
    "log_iter: 20  \n",
    "save_dir: output\n",
    "snapshot_epoch: 1  # 1个epoch输出验证\n",
    "```\n",
    "\n",
    "- optimizer_120e.yml 主要说明了学习率和优化器的配置。\n",
    "文件地址：work/PaddleDetection/configs/ssd/_base_/optimizer_120e.yml\n",
    "```\n",
    "epoch: 20  # 训练轮次\n",
    "\n",
    "LearningRate:\n",
    "  base_lr: 0.001  # 学习率\n",
    "  schedulers:\n",
    "  - !PiecewiseDecay\n",
    "    milestones: [40, 60, 80, 100]\n",
    "    values: [0.001, 0.0005, 0.00025, 0.0001, 0.00001]\n",
    "    use_warmup: false\n",
    "\n",
    "OptimizerBuilder:  # 优化器\n",
    "  optimizer:\n",
    "    momentum: 0.0\n",
    "    type: RMSProp\n",
    "  regularizer:\n",
    "    factor: 0.00005\n",
    "    type: L2\n",
    "```\n",
    "\n",
    "- ssd_mobilenet_v1_300.yml 主要说明模型、和主干网络的情况。\n",
    "文件地址：work/PaddleDetection/configs/ssd/_base_/ssd_mobilenet_v1_300.yml\n",
    "\n",
    "- ssd_mobilenet_reader.yml 主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等-。\n",
    "文件地址：work/PaddleDetection/configs/ssd/_base_/ssd_mobilenet_reader.yml\n",
    "\n",
    "> - 更多详细操作参考[PaddleDetection的github链接](https://github.com/PaddlePaddle/PaddleDetection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 3 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 配置文件在work/PaddleDetection/configs/ssd/ssd_mobilenet_v1_voc.yml，模型保存在work/PaddleDetection/output\r\n",
    "# 可使用可视化工具VisualDL,文件在work/PaddleDetection/vdl_log_dir（需要模型运行时才能产出）\r\n",
    "!python -u tools/train.py -c configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml --use_vdl True --eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "> - 由于时间原因，没训练多少次\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/d202c5e9d71c4a669af3286802a50d91dd154b7db84241e584cf2285b8b78e2d)\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/2a58fb65d2e24427b768f3b84a5305447bbf14f05ce54ad8a4448b27ea89e47e)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 4 模型预测效果展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#测试，查看模型效果，结果存放在work/PaddleDetection/output\r\n",
    "%cd /home/aistudio/work/PaddleDetection/\r\n",
    "!python tools/infer.py -c configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml --infer_img=/home/aistudio/001.jpg -o weights=output/ssd_mobilenet_v1_300_120e_voc/best_model.pdparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio\n"
     ]
    }
   ],
   "source": [
    "%cd /home/aistudio/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "import PIL.Image as Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path='work/PaddleDetection/output/001.jpg'\r\n",
    "img = Image.open(path)\r\n",
    "plt.imshow(img)          #根据数组绘制图像\r\n",
    "plt.show()               #显示图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 5 模型导出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%cd /home/aistudio/work/PaddleDetection/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 导出模型\r\n",
    "!python tools/export_model.py -c configs/ssd/ssd_mobilenet_v1_300_120e_voc.yml -o weights=output/ssd_mobilenet_v1_300_120e_voc/best_model.pdparams --output_dir ./inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 五 PaddleLite生成.nb模型文件\n",
    "[PaddleLite文档](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/android_app_demo.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/\n",
      "Collecting paddlelite\n",
      "\u001b[?25l  Downloading https://mirror.baidu.com/pypi/packages/77/5e/2f463252bcf7de6d594cff2a89f76c643088946203846fbdb845f9cb5378/paddlelite-2.9-cp37-cp37m-manylinux1_x86_64.whl (45.8MB)\n",
      "\u001b[K     |████████████████████████████████| 45.8MB 6.1MB/s eta 0:00:011\n",
      "\u001b[?25hInstalling collected packages: paddlelite\n",
      "Successfully installed paddlelite-2.9\n"
     ]
    }
   ],
   "source": [
    "# 准备PaddleLite依赖\r\n",
    "!pip install paddlelite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/work/PaddleDetection\n"
     ]
    }
   ],
   "source": [
    "%cd /home/aistudio/work/PaddleDetection/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading topology data from inference/ssd_mobilenet_v1_300_120e_voc/model.pdmodel\n",
      "Loading params data from inference/ssd_mobilenet_v1_300_120e_voc/model.pdiparams\n",
      "1. Model is successfully loaded!\n",
      "[W  7/20 19: 8: 3.101 /island/Paddle-Lite/lite/core/program.cc SaveToProgram:135] Unsupported decl type Tensor<host,any,any,0> for var _generated_var_5 in op conditional_block\n",
      "[W  7/20 19: 8: 3.101 /island/Paddle-Lite/lite/core/program.cc SaveToProgram:135] Unsupported decl type Tensor<host,any,any,0> for var _generated_var_2 in op conditional_block\n",
      "2. Model is optimized and saved into ./inference/ssd_mobilenet_v1_300_120e_voc.nb successfully\n"
     ]
    }
   ],
   "source": [
    "# 准备PaddleLite部署模型\r\n",
    "#--valid_targets中参数（arm）用于传统手机，（npu,arm ）用于华为带有npu处理器的手机\r\n",
    "!paddle_lite_opt \\\r\n",
    "    --model_file=inference/ssd_mobilenet_v1_300_120e_voc/model.pdmodel \\\r\n",
    "    --param_file=inference/ssd_mobilenet_v1_300_120e_voc/model.pdiparams \\\r\n",
    "    --optimize_out=./inference/ssd_mobilenet_v1_300_120e_voc \\\r\n",
    "    --optimize_out_type=naive_buffer \\\r\n",
    "    --valid_targets=arm \r\n",
    "    #--valid_targets=npu,arm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 六 安卓端部署"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 1 准备Android Studio开发环境（安装系统为Ubuntu 64-bit，Win用户可以安装虚拟机进行操作）\n",
    "- 快捷键Ctrl+Alt+t打开终端，安装64位所需库\n",
    "```\n",
    "sudo apt-get install libc6:i386 libncurses5:i386 libstdc++6:i386 lib32z1 libbz2-1.0:i386\n",
    "```\n",
    "- 安装java环境（作者使用的是OpenJDK 8 ）\n",
    "\n",
    "查看java版本\n",
    "```\n",
    "java --version  \n",
    "```\n",
    "下载OpenJDK 8\n",
    "```\n",
    "sudo apt-get install openjdk-8-jdk\n",
    "```\n",
    "若系统存在多个版本的java，输入对应选项的数字切换Java版本（初次安装则不需要执行下面）\n",
    "```\n",
    "update-alternatives --config java\n",
    "```\n",
    "- 安装cmake和ninja环境\n",
    "```\n",
    "# root用户进入命令\n",
    "sudo su\n",
    "# root用户退出命令\n",
    "exit\n",
    "```\n",
    "```\n",
    "# 1. Install basic software 参考官方文档，注意权限，应该是root用户\n",
    "apt update\n",
    "apt-get install -y --no-install-recommends \\\n",
    "  gcc g++ git make wget python unzip adb curl\n",
    "\n",
    "# 2. Install cmake 3.10 or above 参考官方文档，注意权限，应该是root用户\n",
    "wget -c https://mms-res.cdn.bcebos.com/cmake-3.10.3-Linux-x86_64.tar.gz && \\\n",
    "    tar xzf cmake-3.10.3-Linux-x86_64.tar.gz && \\\n",
    "    mv cmake-3.10.3-Linux-x86_64 /opt/cmake-3.10 && \\  \n",
    "    ln -s /opt/cmake-3.10/bin/cmake /usr/bin/cmake && \\\n",
    "    ln -s /opt/cmake-3.10/bin/ccmake /usr/bin/ccmake\n",
    "    \n",
    "# 3. Install ninja-build 此处需退出root用户\n",
    "sudo apt-get install ninja-build\n",
    "```\n",
    "- Android Studio软件安装流程参考官方视频：\n",
    "[Ubuntu上推荐的设置流程](https://developer.android.google.cn/studio/videos/studio-install-linux.mp4?hl=zh-cn)\n",
    "\n",
    "> 注意：安装过程中会如果出现下面的报错`unable to access android sdk add-on lis`，点击`cancel`跳过，这是因为没有事先安装SDK的原因，我们稍后在Android Studio中进行安装。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 2 导入Paddle-Lite-Demo\n",
    "- 在自己的PC上下载好Paddle-Lite-Demo项目（下面的代码新开一个终端执行，不要在运行Android Studio的终端上操作）\n",
    "```\n",
    "git clone https://gitee.com/paddlepaddle/Paddle-Lite-Demo.git\n",
    "```\n",
    "- 在Android Studio中导入PaddleLite-android-demo目录下object_detection_demo项目，如下图所示\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/5a3e9a690cc241a7a31726999041487d9d6126ca586e43eeb3288d4bdb88143d)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 3 配置SDK和NDK\n",
    "- 在开发界面点击File->Settings... 在搜索框中输入sdk\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/cc469b4918f941898293159bfd895227823d7dede0fd44b8a605cc077178974f)\n",
    "\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/6212319e0c2b427692963692167878e780bc0f2c2b514cb4b397dda10d93a2ce)\n",
    "\n",
    "- 在开发界面点击File->Project Structure,选取刚刚下载的NDK路径\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/9e2529ce745b41eba29ece05a8e1b563f8db5f0621f04c7a81365f8fa7ffcb49)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 4 模型和标签的拷贝\n",
    "- 模型拷贝\n",
    "到PaddleLite-android-demo/object_detection_demo/app/src/main/assets/models目录下，新建ssd_mobv1_mask文件夹，将刚刚生成的.nb文件拷贝到该文佳夹下，并重命名为model.nb,如下图所示。（模型文件我挂载项目的data数据集里了，可先尝试一下）\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/c9c227435c7840339c25e1b4a39bc3613c805dd5b1f54347a90fdb93d6bbfcdd)\n",
    "- 标签拷贝\n",
    "到PaddleLite-android-demo/object_detection_demo/app/src/main/assets/labels目录下，仿照已有.txt文件新建一个face.txt文件（注意label文件中是background+你数据集的label）,内容如下\n",
    "```\n",
    "background\n",
    "face\n",
    "face_mask\n",
    "```\n",
    "- 拷贝一张测试图片\n",
    "到PaddleLite-android-demo/object_detection_demo/app/src/main/assets/images目录下，拷贝一张项目初始化测试的图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 5 修改项目文件\n",
    "- 修改PaddleLite-android-demo/object_detection_demo/app/src/main/res/values/strings.xml中对应的参数\n",
    "- MODEL_DIR_DEFAULT  模型路径\n",
    "- LABEL_PATH_DEFAULT  标签路径\n",
    "- IMAGE_PATH_DEFAULT  项目初始化测试图片路径\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/87d9f0665b3948ff861d5b7b7ff317d49eb76a98060446c2b6b12792d5c836df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 6 准备手机\n",
    "- 将手机调为开发者模式，将手机通过USB数据线连接到PC上，点击运行，等待一会就好了\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/5313783414c44aa9a4b42d4e9f0eb89b1f13e037964943c3a3e5fcd63169ce18)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 7 效果展示\n",
    "- 项目初始界面\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/5f0d0f51405140f5851ea64df667550a55b7c04e2cda428e9072ef3a5d1ee01c)\n",
    "\n",
    "- 项目通过相机拍取画面（因为拍的是电脑屏幕，看起来有点花）\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/0393f99bd4c8424792a7b364591674d0d735a2b92f104a7794299c95fa432ff7)\n",
    "\n",
    "- 项目通过调取手机相册的图片\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/0ef09fa72c154de38011768e929da42c30100be8f1b84298adbd1cf12d44aaa1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 8 apk打包\n",
    "- 在工具栏中点击如下\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/95f2e78c14884f848f9fe74876cc35a76a7c8db4c8e344f391254c5799d7b098)\n",
    "\n",
    "- 选择打包成apk\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/3402e2b846eb40e3af641310570336f0beba431d9b6e45acbf1acca25d3bf214)\n",
    "\n",
    "- 若第一次打包，需创建新的秘钥文件\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/f3815daadd0745fe8c205941b53f53d13c59892007704279bba41af6e4a55233)\n",
    "\n",
    "- 创建成功后\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/06d714fce76946e4b0ccb046ccfe1aacd459e5d0d28c4f058a04030304ab2671)\n",
    "\n",
    "- next，v1、v2都勾选上\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/c2c9e656978841dd9f27a95364a7e433f647d8076beb47be8200976527425ef0)\n",
    "\n",
    "- 打包成功\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/164757219405423ca713a81271451b3b0c8bbe2c6ed8458abc6662348e01ae63)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 小结\n",
    "> - 本次部署选择object_detection_demo是考虑到有的小伙伴想做其它方面的检测，给大家介绍一个通用的检测demo，若专门做口罩检测可选择mask_detection_demo\n",
    "> - 如若存在问题，可在评论区留言，作者会不时为大家讲解\n",
    "> - 作者aistudio主页链接，欢迎各位互粉、提问：[aistudio](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/539945)\n",
    "> - 完结（#￣▽￣#）撒花"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  <br>\n",
    "Please click [here ](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions. "
   ]
  }
 ],
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